from __future__ import absolute_import, division, print_function

import torch
from torch.distributions import constraints
from torch.nn import Parameter

import pyro
import pyro.distributions as dist
from pyro.contrib import autoname
from pyro.contrib.gp.models.model import GPModel
from pyro.contrib.gp.util import conditional
from pyro.distributions.util import eye_like


class VariationalGP(GPModel):
    r"""
    Variational Gaussian Process model.

    This model deals with both Gaussian and non-Gaussian likelihoods. Given inputs\
    :math:`X` and their noisy observations :math:`y`, the model takes the form

    .. math::
        f &\sim \mathcal{GP}(0, k(X, X)),\\
        y & \sim p(y) = p(y \mid f) p(f),

    where :math:`p(y \mid f)` is the likelihood.

    We will use a variational approach in this model by approximating :math:`q(f)` to
    the posterior :math:`p(f\mid y)`. Precisely, :math:`q(f)` will be a multivariate
    normal distribution with two parameters ``f_loc`` and ``f_scale_tril``, which will
    be learned during a variational inference process.

    .. note:: This model can be seen as a special version of
        :class:`.SparseVariationalGP` model with :math:`X_u = X`.

    .. note:: This model has :math:`\mathcal{O}(N^3)` complexity for training,
        :math:`\mathcal{O}(N^3)` complexity for testing. Here, :math:`N` is the number
        of train inputs. Size of variational parameters is :math:`\mathcal{O}(N^2)`.

    :param torch.Tensor X: A input data for training. Its first dimension is the number
        of data points.
    :param torch.Tensor y: An output data for training. Its last dimension is the
        number of data points.
    :param ~pyro.contrib.gp.kernels.kernel.Kernel kernel: A Pyro kernel object, which
        is the covariance function :math:`k`.
    :param ~pyro.contrib.gp.likelihoods.likelihood Likelihood likelihood: A likelihood
        object.
    :param callable mean_function: An optional mean function :math:`m` of this Gaussian
        process. By default, we use zero mean.
    :param torch.Size latent_shape: Shape for latent processes (`batch_shape` of
        :math:`q(f)`). By default, it equals to output batch shape ``y.shape[:-1]``.
        For the multi-class classification problems, ``latent_shape[-1]`` should
        corresponse to the number of classes.
    :param bool whiten: A flag to tell if variational parameters ``f_loc`` and
        ``f_scale_tril`` are transformed by the inverse of ``Lff``, where ``Lff`` is
        the lower triangular decomposition of :math:`kernel(X, X)`. Enable this flag
        will help optimization.
    :param float jitter: A small positive term which is added into the diagonal part of
        a covariance matrix to help stablize its Cholesky decomposition.
    """
    def __init__(self, X, y, kernel, likelihood, mean_function=None,
                 latent_shape=None, whiten=False, jitter=1e-6):
        super(VariationalGP, self).__init__(X, y, kernel, mean_function, jitter)

        self.likelihood = likelihood

        y_batch_shape = self.y.shape[:-1] if self.y is not None else torch.Size([])
        self.latent_shape = latent_shape if latent_shape is not None else y_batch_shape

        N = self.X.size(0)
        f_loc = self.X.new_zeros(self.latent_shape + (N,))
        self.f_loc = Parameter(f_loc)

        identity = eye_like(self.X, N)
        f_scale_tril = identity.repeat(self.latent_shape + (1, 1))
        self.f_scale_tril = Parameter(f_scale_tril)
        self.set_constraint("f_scale_tril", constraints.lower_cholesky)

        self.whiten = whiten
        self._sample_latent = True

    @autoname.scope(prefix="VGP")
    def model(self):
        self.set_mode("model")

        N = self.X.size(0)
        Kff = self.kernel(self.X).contiguous()
        Kff.view(-1)[::N + 1] += self.jitter  # add jitter to the diagonal
        Lff = Kff.cholesky()

        zero_loc = self.X.new_zeros(self.f_loc.shape)
        if self.whiten:
            identity = eye_like(self.X, N)
            pyro.sample("f",
                        dist.MultivariateNormal(zero_loc, scale_tril=identity)
                            .to_event(zero_loc.dim() - 1))
            f_scale_tril = Lff.matmul(self.f_scale_tril)
            f_loc = Lff.matmul(self.f_loc.unsqueeze(-1)).squeeze(-1)
        else:
            pyro.sample("f",
                        dist.MultivariateNormal(zero_loc, scale_tril=Lff)
                            .to_event(zero_loc.dim() - 1))
            f_scale_tril = self.f_scale_tril
            f_loc = self.f_loc

        f_loc = f_loc + self.mean_function(self.X)
        f_var = f_scale_tril.pow(2).sum(dim=-1)
        if self.y is None:
            return f_loc, f_var
        else:
            return self.likelihood(f_loc, f_var, self.y)

    @autoname.scope(prefix="VGP")
    def guide(self):
        self.set_mode("guide")

        pyro.sample("f",
                    dist.MultivariateNormal(self.f_loc, scale_tril=self.f_scale_tril)
                        .to_event(self.f_loc.dim()-1))

    def forward(self, Xnew, full_cov=False):
        r"""
        Computes the mean and covariance matrix (or variance) of Gaussian Process
        posterior on a test input data :math:`X_{new}`:

        .. math:: p(f^* \mid X_{new}, X, y, k, f_{loc}, f_{scale\_tril})
            = \mathcal{N}(loc, cov).

        .. note:: Variational parameters ``f_loc``, ``f_scale_tril``, together with
            kernel's parameters have been learned from a training procedure (MCMC or
            SVI).

        :param torch.Tensor Xnew: A input data for testing. Note that
            ``Xnew.shape[1:]`` must be the same as ``self.X.shape[1:]``.
        :param bool full_cov: A flag to decide if we want to predict full covariance
            matrix or just variance.
        :returns: loc and covariance matrix (or variance) of :math:`p(f^*(X_{new}))`
        :rtype: tuple(torch.Tensor, torch.Tensor)
        """
        self._check_Xnew_shape(Xnew)
        self.set_mode("guide")

        loc, cov = conditional(Xnew, self.X, self.kernel, self.f_loc, self.f_scale_tril,
                               full_cov=full_cov, whiten=self.whiten, jitter=self.jitter)
        return loc + self.mean_function(Xnew), cov
